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Article type: Research Article
Authors: Xu, Xiaoyanga; * | Yang, Zhengrongb | Hao, Lizhongc
Affiliations: [a] School of Finance and Economics, Jiangsu University, Zhenjiang, China | [b] Department of Public Courses, Zhenjiang College, Zhenjiang, China | [c] Pamplin School of Business Administration, University of Portland, Portland, USA
Correspondence: [*] Corresponding author. Xiaoyang Xu, School of Finance and Economics, Jiangsu University, Zhenjiang, 212013, China. Tel./Fax: +86 0511 88780086; E-mail: ujsxxy@163.com.
Abstract: Since reforming and opening up to the world, China’s economy has been developing rapidly, and industries have become the basis, priority and pillar ones of national economic development. Meanwhile, industries have the characteristics of high investment cost, long payback period and high investment risks. Especially in recent years the government has adopted a series of macro control policies which have had a significant impact on the risk investment project. Therefore, it is an important problem for the real estate companies to evaluate investment risks of risk investment project scientifically and reasonably and to take appropriate measures, and the research on theories and methods of risk assessment of risk investment project has important theoretical and practical significance. In this paper, we study on the multiple attribute decision making problems to risk evaluation of the risk investment project based on the investor sentiment with triangular fuzzy information. Then, we have developed the triangular fuzzy induced Einstein ordered weighted averaging (TFIEOWA) operator. We have exploited the TFIEOWA operator to multiple attribute decision making for risk evaluation of the risk investment project based on the investor sentiment with triangular fuzzy information. In the end, an example is proposed to prove the effectiveness of the proposed approach.
Keywords: Multiple attribute decision making, triangular fuzzy number, risk evaluation, risk investment project
DOI: 10.3233/JIFS-161111
Journal: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 6, pp. 3201-3208, 2017
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